Clusters & Sub-clusters

6 FAIR data and materials

7 sub-clusters · 82 references

Students will learn about FAIR data (and education/research materials) principles that address Findability, Accessibility, Interoperability, and Reusability; engage with reasons to share data, the initiatives designed to increase scientific openness; as well as of possible privacy and security considerations together with anonymization procedures. There are 7 sub-clusters which aim to further parse the learning and teaching process:

Reasons to share data and materials

Sharing data and research materials is beneficial for science and society. Open data can enable validation of results, inspire new discoveries through re-use, increase researcher credit (e.g., via data citations), and promote transparency that enhances trust. Key studies have shown that papers with shared data receive more citations and foster broader collaboration.

  • Bishop, L., & Kuula-Luumi, A. (2017). Revisiting Qualitative Data Reuse. Sage Open, 7(1). https://doi.org/10.1177/2158244016685136
  • Colavizza, G., Hrynaszkiewicz, I., Staden, I., Whitaker, K., & McGillivray, B. (2020). The citation advantage of linking publications to research data. PLOS ONE, 15(4), e0230416. https://doi.org/10.1371/journal.pone.0230416
  • Dienlin, T., Johannes, N., Bowman, N. D., Masur, P. K., Engesser, S., Kümpel, A. S., Lukito, J., Bier, L. M., Zhang, R., Johnson, B. K., Huskey, R., Schneider, F. M., Breuer, J., Parry, D. A., Vermeulen, I., Fisher, J. T., Banks, J., Weber, R., Ellis, D. A., … de Vreese, C. (2020). An Agenda for Open Science in Communication. Journal of Communication, 71(1), 1–26. https://doi.org/10.1093/joc/jqz052
  • DuBois, J. M., Strait, M., & Walsh, H. (2018). Is it time to share qualitative research data? Qualitative Psychology, 5(3), 380–393. https://doi.org/10.1037%2Fqup0000076
  • Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., Aveyard, M., Axt, J. R., Babalola, M. T., Bahník, Š., Batra, R., Berkics, M., Bernstein, M. J., Berry, D. R., Bialobrzeska, O., Binan, E. D., Bocian, K., Brandt, M. J., Busching, R., … Nosek, B. A. (2018). Many Labs 2: Investigating Variation in Replicability Across Samples and Settings. Advances in Methods and Practices in Psychological Science, 1(4), 443–490. https://doi.org/10.1177/2515245918810225
  • Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Bahník, Š., Bernstein, M. J., Bocian, K., Brandt, M. J., Brooks, B., Brumbaugh, C. C., Cemalcilar, Z., Chandler, J., Cheong, W., Davis, W. E., Devos, T., Eisner, M., Frankowska, N., Furrow, D., Galliani, E. M., … Nosek, B. A. (2014). Investigating Variation in Replicability. Social Psychology, 45(3), 142–152. https://doi.org/10.1027/1864-9335/a000178
  • Levenstein, M. C., & Lyle, J. A. (2018). Data: Sharing Is Caring. Advances in Methods and Practices in Psychological Science, 1(1), 95–103. https://doi.org/10.1177/2515245918758319
  • Transparency: The Emerging Third Dimension of Open Science and Open Data. (2016). LIBER QUARTERLY, 25(4), 153–171. https://doi.org/10.18352/lq.10113
  • McKiernan, E. C., Bourne, P. E., Brown, C. T., Buck, S., Kenall, A., Lin, J., McDougall, D., Nosek, B. A., Ram, K., Soderberg, C. K., Spies, J. R., Thaney, K., Updegrove, A., Woo, K. H., & Yarkoni, T. (2016). How open science helps researchers succeed. ELife, 5. CLOCKSS. https://doi.org/10.7554/eLife.16800
  • Peng, R. (2015). The Reproducibility Crisis in Science: A Statistical Counterattack. Significance, 12(3), 30–32. https://doi.org/10.1111/j.1740-9713.2015.00827.x
  • Piwowar, H. A., & Vision, T. J. (2013). Data reuse and the open data citation advantage. PeerJ, 1, e175. https://doi.org/10.7717/peerj.175
  • Rosenberg, J. M., Borchers, C., Burchfield, M. A., Anderson, D., Stegenga, S. M., & Fischer, C. (2022). Posts About Students on Facebook: A Data Ethics Perspective. Educational Researcher, 51(8), 547–550. https://doi.org/10.3102/0013189X221120538
  • Rouder, J. N. (2015). The what, why, and how of born-open data. Behavior Research Methods, 48(3), 1062–1069. https://doi.org/10.3758/s13428-015-0630-z
  • Stodden, V. C. (2011). Trust Your Science? Open Your Data and Code. Columbia University. https://doi.org/10.7916/D8CJ8Q0P
  • Tennant, J. P., Waldner, F., Jacques, D. C., Masuzzo, P., Collister, L. B., & Hartgerink, Chris. H. J. (2016). The academic, economic and societal impacts of Open Access: an evidence-based review. F1000Research, 5, 632. https://doi.org/10.12688/f1000research.8460.3
  • Santana, C. (2024). The Value of Openness in Open Science. Canadian Journal of Philosophy, 54(4), 251–265. https://doi.org/10.1017/can.2024.44
  • Staunton, C., Barragán, C. A., Canali, S., Ho, C., Leonelli, S., Mayernik, M., Prainsack, B., & Wonkham, A. (2021). Open science, data sharing and solidarity: who benefits? History and Philosophy of the Life Sciences, 43(4). https://doi.org/10.1007/s40656-021-00468-6
  • Tsai, A. C., Kohrt, B. A., Matthews, L. T., Betancourt, T. S., Lee, J. K., Papachristos, A. V., Weiser, S. D., & Dworkin, S. L. (2016). Promises and pitfalls of data sharing in qualitative research. Social Science & Medicine, 169, 191–198. https://doi.org/10.1016/j.socscimed.2016.08.004

Reasons not to share: Privacy and security considerations

Open sharing of data sometimes poses legitimate privacy and security concerns. These include protecting participant privacy, honoring cultural ownership of data, and security risks. It emphasizes that not all data can or should be open, and ethical frameworks guide decisions in these cases.

  • Branney, P., Reid, K., Frost, N., Coan, S., Mathieson, A., & Woolhouse, M. (2019). A context-consent meta-framework for designing open (qualitative) data studies. Qualitative Research in Psychology, 16(3), 483–502. https://doi.org/10.1080/14780887.2019.1605477
  • Chauvette, A., Schick-Makaroff, K., & Molzahn, A. E. (2019). Open Data in Qualitative Research. International Journal of Qualitative Methods, 18. https://doi.org/10.1177/1609406918823863
  • Field, S. M. (2025). Open With Care! Consent, Context, and Co-production in Open Qualitative Research. https://doi.org/10.31219/osf.io/6z9c3_v1
  • Gow, J., Moffatt, C., & Blackport, J. (2020). Participation in patient support forums may put rare disease patient data at risk of re-identification. Orphanet Journal of Rare Diseases, 15(1). https://doi.org/10.1186/s13023-020-01497-3
  • Hand, D. J. (2018). Aspects of Data Ethics in a Changing World: Where Are We Now? Big Data, 6(3), 176–190. https://doi.org/10.1089/big.2018.0083
  • Jennings, L., Anderson, T., Martinez, A., Sterling, R., Chavez, D. D., Garba, I., Hudson, M., Garrison, N. A., & Carroll, S. R. (2023). Applying the ‘CARE Principles for Indigenous Data Governance’ to ecology and biodiversity research. Nature Ecology & Evolution, 7(10), 1547–1551. https://doi.org/10.1038/s41559-023-02161-2
  • Jacobs, A. M., Büthe, T., Arjona, A., Arriola, L. R., Bellin, E., Bennett, A., Björkman, L., Bleich, E., Elkins, Z., Fairfield, T., Gaikwad, N., Greitens, S. C., Hawkesworth, M., Herrera, V., Herrera, Y. M., Johnson, K. S., Karakoç, E., Koivu, K., Kreuzer, M., … Yashar, D. J. (2021). The Qualitative Transparency Deliberations: Insights and Implications. Perspectives on Politics, 19(1), 171–208. https://doi.org/10.1017/S1537592720001164
  • Jao, I., Kombe, F., Mwalukore, S., Bull, S., Parker, M., Kamuya, D., Molyneux, S., & Marsh, V. (2015). Involving Research Stakeholders in Developing Policy on Sharing Public Health Research Data in Kenya. Journal of Empirical Research on Human Research Ethics, 10(3), 264–277. https://doi.org/10.1177/1556264615592385
  • Jao, I., Kombe, F., Mwalukore, S., Bull, S., Parker, M., Kamuya, D., Molyneux, S., & Marsh, V. (2015). Research Stakeholders’ Views on Benefits and Challenges for Public Health Research Data Sharing in Kenya: The Importance of Trust and Social Relations. PLOS ONE, 10(9), e0135545. https://doi.org/10.1371/journal.pone.0135545
  • Joel, S., Eastwick, P. W., & Finkel, E. J. (2018). Open Sharing of Data on Close Relationships and Other Sensitive Social Psychological Topics: Challenges, Tools, and Future Directions. Advances in Methods and Practices in Psychological Science, 1(1), 86–94. https://doi.org/10.1177/2515245917744281
  • Khan, S., Hirsch, J. S., & Zeltzer-Zubida, O. (2024). A dataset without a code book: ethnography and open science. Frontiers in Sociology, 9. https://doi.org/10.3389/fsoc.2024.1308029
  • Kirilova, D., & Karcher, S. (2017). Rethinking Data Sharing and Human Participant Protection in Social Science Research: Applications from the Qualitative Realm. Data Science Journal, 16. https://doi.org/10.5334/dsj-2017-043
  • Crespo López, M. de los Á., Pallise Perello, C., de Ridder, J., & Labib, K. (2025). Open Science as Confused: Contradictory and Conflicting Discourses in Open Science Guidance to Researchers. https://doi.org/10.31222/osf.io/zr35u_v1
  • Lamb, D., Russell, A., Morant, N., & Stevenson, F. (2024). The challenges of open data sharing for qualitative researchers. Journal of Health Psychology, 29(7), 659–664. https://doi.org/10.1177/13591053241237620
  • Long-Sutehall, T., Sque, M., & Addington-Hall, J. (2010). Secondary analysis of qualitative data: a valuable method for exploring sensitive issues with an elusive population? Journal of Research in Nursing, 16(4), 335–344. https://doi.org/10.1177/1744987110381553
  • McGrath, C., & Nilsonne, G. (2018). Data sharing in qualitative research: Opportunities and concerns. MedEdPublish, 7(255), 255.
  • McIntosh, B., Ichikawa, K., & Nelson, N. C. (2025). Adversarial reanalysis and the challenge of open data in regulatory science. https://doi.org/10.31222/osf.io/jfbr8_v1
  • Mozersky, J., Parsons, M., Walsh, H., Baldwin, K., McIntosh, T., & DuBois, J. M. (2020). Research Participant Views regarding Qualitative Data Sharing. Ethics & Human Research, 42(2), 13–27. Portico. https://doi.org/10.1002/eahr.500044
  • O’Callaghan, E., & Douglas, H. M. (2021). #MeToo Online Disclosures: A Survivor-Informed Approach to Open Science Practices and Ethical Use of Social Media Data. Psychology of Women Quarterly, 45(4), 505–525. https://doi.org/10.1177/03616843211039175
  • Prosser, A. M., Bagnall, R., & Higson-Sweeney, N. (2024). Reflection over compliance: Critiquing mandatory data sharing policies for qualitative research. Journal of Health Psychology, 29(7), 653–658. https://doi.org/10.1177/13591053231225903
  • Prosser, A. M. B., Hamshaw, R., Meyer, J., Bagnall, R., Blackwood, L., Huysamen, M., Jordan, A., Vasileiou, K., & Walter, Z. (2021). When open data closes the door: A critical examination of the past, present and the potential future for open data guidelines in journals. https://doi.org/10.31234/osf.io/5yw4z
  • Ross, M. W., Iguchi, M. Y., & Panicker, S. (2018). Ethical aspects of data sharing and research participant protections. American Psychologist, 73(2), 138–145. https://doi.org/10.1037/amp0000240
  • Siler, K., Haustein, S., Smith, E., Larivière, V., & Alperin, J. P. (2018). Authorial and institutional stratification in open access publishing: the case of global health research. PeerJ, 6, e4269. Portico. https://doi.org/10.7717/peerj.4269
  • Strech, D., Haven, T., Madai, V. I., Meurers, T., & Prasser, F. (2023). Generating evidence on privacy outcomes to inform privacy risk management: A way forward? Journal of Biomedical Informatics, 137, 104257. https://doi.org/10.1016/j.jbi.2022.104257
  • VandeVusse, A., Mueller, J., & Karcher, S. (2021). Qualitative Data Sharing: Participant Understanding, Motivation, and Consent. Qualitative Health Research, 32(1), 182–191. https://doi.org/10.1177/10497323211054058
  • Vaz, M., Palmero, A. G., Nyangulu, W., Diallo, A. A., & Ho, C. W. L. (2019). Diffusion of ethical governance policy on sharing of biological materials and related data for biomedical research. Wellcome Open Research, 4, 170. https://doi.org/10.12688/wellcomeopenres.15480.1
  • Walsh, C. G., Xia, W., Li, M., Denny, J. C., Harris, P. A., & Malin, B. A. (2018). Enabling Open-Science Initiatives in Clinical Psychology and Psychiatry Without Sacrificing Patients’ Privacy: Current Practices and Future Challenges. Advances in Methods and Practices in Psychological Science, 1(1), 104–114. https://doi.org/10.1177/2515245917749652

Licenses and reuse

Licensing determines how others may access, cite, remix, and redistribute your work. This section orients you to data/code/materials licenses (e.g., CC BY/CC0), data-use agreements, and rights/obligations that shape ethical, legally sound reuse, especially for qualitative and sensitive data

  • Anon. (n.d.). Can I reuse someone else’s research data? OpenAIRE. https://www.openaire.eu/can-i-reuse-someone-else-research-data
  • FIELDING, N. (2004). Getting the most from archived qualitative data: epistemological, practical and professional obstacles. International Journal of Social Research Methodology, 7(1), 97–104. https://doi.org/10.1080/13645570310001640699
  • Hagedorn, G., Mietchen, D., Morris, R., Agosti, D., Penev, L., Berendsohn, W., & Hobern, D. (2011). Creative Commons licenses and the non-commercial condition: Implications for the re-use of biodiversity information. ZooKeys, 150, 127–149. https://doi.org/10.3897/zookeys.150.2189
  • Houtkoop, B. L., Chambers, C., Macleod, M., Bishop, D. V. M., Nichols, T. E., & Wagenmakers, E.-J. (2018). Data Sharing in Psychology: A Survey on Barriers and Preconditions. Advances in Methods and Practices in Psychological Science, 1(1), 70–85. https://doi.org/10.1177/2515245917751886
  • Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., Aveyard, M., Axt, J. R., Babalola, M. T., Bahník, Š., Batra, R., Berkics, M., Bernstein, M. J., Berry, D. R., Bialobrzeska, O., Binan, E. D., Bocian, K., Brandt, M. J., Busching, R., … Nosek, B. A. (2018). Many Labs 2: Investigating Variation in Replicability Across Samples and Settings. Advances in Methods and Practices in Psychological Science, 1(4), 443–490. https://doi.org/10.1177/2515245918810225
  • Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Bahník, Š., Bernstein, M. J., Bocian, K., Brandt, M. J., Brooks, B., Brumbaugh, C. C., Cemalcilar, Z., Chandler, J., Cheong, W., Davis, W. E., Devos, T., Eisner, M., Frankowska, N., Furrow, D., Galliani, E. M., … Nosek, B. A. (2014). Investigating Variation in Replicability. Social Psychology, 45(3), 142–152. https://doi.org/10.1027/1864-9335/a000178
  • Mozersky, J., McIntosh, T., Walsh, H. A., Parsons, M. V., Goodman, M., & DuBois, J. M. (2021). Barriers and facilitators to qualitative data sharing in the United States: A survey of qualitative researchers. PLOS ONE, 16(12), e0261719. https://doi.org/10.1371/journal.pone.0261719
  • Stodden, V. (2009). The Legal Framework for Reproducible Scientific Research: Licensing and Copyright. Computing in Science & Engineering, 11(1), 35–40. https://doi.org/10.1109/MCSE.2009.19
  • Wicherts, J. M., Borsboom, D., Kats, J., & Molenaar, D. (2006). The poor availability of psychological research data for reanalysis. American Psychologist, 61(7), 726–728. https://doi.org/10.1037/0003-066X.61.7.726
  • Whylly, K. E., Karcher, S., & Renbarger, R. (2023, January 25). Data sharing for qualitative research: Webinar and panel. Center for Open Science. https://youtu.be/eWZvmSIfhQY
  • Wicherts, J. M., Veldkamp, C. L. S., Augusteijn, H. E. M., Bakker, M., van Aert, R. C. M., & van Assen, M. A. L. M. (2016). Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01832

Metadata standards

Reusable research starts with good, machine-actionable metadata. This sub-cluster points to field-tested schemas and “minimum information” checklists so teams can capture provenance, methods, and context consistently across datasets, code, and teaching materials.

  • Anon. (n.d.). Fair cookbook. FAIR Cookbook. https://faircookbook.elixir-europe.org/content/home.html
  • Anon. (n.d.). Project metadata. LDbase. https://www.ldbase.org/resources/user-guide/information-to-gather/project-metadata
  • Rocca-Serra, P., Gu, W., Ioannidis, V., Abbassi-Daloii, T., Capella-Gutierrez, S., Chandramouliswaran, I., Splendiani, A., Burdett, T., Giessmann, R. T., Henderson, D., Batista, D., Emam, I., Gadiya, Y., Giovanni, L., Willighagen, E., Evelo, C., Gray, A. J. G., Gribbon, P., Juty, N., … Sansone, S.-A. (2023). The FAIR Cookbook - the essential resource for and by FAIR doers. Scientific Data, 10(1). https://doi.org/10.1038/s41597-023-02166-3
  • Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1). https://doi.org/10.1038/sdata.2016.18

Repositories

Trusted places to deposit datasets, code, and teaching materials so they remain findable, citable, and preserved.

Research data management

Introduces the planning and processes for managing research data through its lifecycle, from organizing files and documenting data (so that you and others can understand it later) to storing it securely and preparing it for sharing or archiving. Good RDM underpins the ability to be FAIR.

  • Demgenski, R., Karcher, S., Kirilova, D., & Weber, N. (2021). Introducing the Qualitative Data Repository’s Curation Handbook. Journal of EScience Librarianship, 10(3). https://doi.org/10.7191/jeslib.2021.1207
  • Harmon-Jones, E., Harmon-Jones, C., Amodio, D. M., Gable, P. A., & Schmeichel, B. J. (2025). Valid replications require valid methods: Recommendations for best methodological practices with lab experiments. Motivation Science, 11(3), 235–245. https://doi.org/10.1037/mot0000398
  • Hepkema, W. M., Horbach, S. P. J. M., Hoek, J. M., & Halffman, W. (2021). Misidentified biomedical resources: Journal guidelines are not a quick fix. International Journal of Cancer, 150(8), 1233–1243. Portico. https://doi.org/10.1002/ijc.33882
  • Karcher, S., Kirilova, D., Pagé, C., & Weber, N. (2021). How Data Curation Enables Epistemically Responsible Reuse of Qualitative Data. The Qualitative Report. https://doi.org/10.46743/2160-3715/2021.5012
  • Mannheimer, S., Pienta, A., Kirilova, D., Elman, C., & Wutich, A. (2018). Qualitative Data Sharing: Data Repositories and Academic Libraries as Key Partners in Addressing Challenges. American Behavioral Scientist, 63(5), 643–664. https://doi.org/10.1177/0002764218784991
  • Michener, W. K. (2015). Ten Simple Rules for Creating a Good Data Management Plan. PLOS Computational Biology, 11(10), e1004525. https://doi.org/10.1371/journal.pcbi.1004525
  • Rouder, J. N. (2015). The what, why, and how of born-open data. Behavior Research Methods, 48(3), 1062–1069. https://doi.org/10.3758/s13428-015-0630-z
  • UK Data Service. (n.d.). Research data management., https://ukdataservice.ac.uk/learning-hub/research-data-management/
  • van Ravenzwaaij, D., de Jong, M., Hoekstra, R., Scheibe, S., Span, M. M., Wessel, I., & Heininga, V. E. (2025). De-Identification When Making Data Sets Findable, Accessible, Interoperable, and Reusable (FAIR): Two Worked Examples From the Behavioral and Social Sciences. Advances in Methods and Practices in Psychological Science, 8(2), 25152459251336130. https://doi.org/10.1177/251524592513361

FAIR principles applied to Education & Training

FAIR isn’t only for datasets, syllabi, slides, and assignments can be findable, accessible, interoperable, and reusable too. This section offers institutional and practical roadmaps to make FAIR-by-design teaching materials the default.

  • Filiposka, S., Green, D., Mishev, A., Kjorveziroski, V., Corleto, A., Napolitano, E., Paolini, G., Di Giorgio, S., Janik, J., Schirru, L., Gingold, A., Hadrossek, C., Souyioultzoglou, I., Leister, C., Pavone, G., Sharma, S., Mendez Rodriguez, E. M., & Lazzeri, E. (2023). D2.2 Methodology for FAIR-by-Design Training Materials. https://doi.org/10.5281/zenodo.8305540
  • Funk, E. M., Toelch, U., Ludwig, R., & Kniffert, S. (2025). Ten quick tips for navigating intellectual property in FAIR educational resources. PLOS Computational Biology, 21(7), e1013208. https://doi.org/10.1371/journal.pcbi.1013208
  • Kohrs, F. E., Auer, S., Bannach-Brown, A., Fiedler, S., Haven, T. L., Heise, V., Holman, C., Azevedo, F., Bernard, R., Bleier, A., Bössel, N., Cahill, B. P., Castro, L. J., Ehrenhofer, A., Eichel, K., Frank, M., Frick, C., Friese, M., Gärtner, A., … Weissgerber, T. L. (2023). Eleven strategies for making reproducible research and open science training the norm at research institutions. ELife, 12. CLOCKSS. https://doi.org/10.7554/eLife.89736
  • Provost, L., Bezuidenhout, L., Venkataraman, S., van der Lek, I., van Gelder, C., Kuchma, I., Leenarts, E., Azevedo, F., Brvar, I. V., Paladin, L., Clare, H., & Braukmann, R. (2024). Towards FAIRification of learning resources and catalogues—lessons learnt from research communities. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1390444
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